I have always believed that a true techie is like a hotshot pilot—the thrill of new technology is the only thing that matters. The relevance of that new technology to the project does not always come into play.

I was recently talking to a talented techie who was working on a database design/data model for a small application. He was advised to use an open source RDBMS for DB design, but he was not convinced this was the way to go. He wanted to use NextGen database (NoSQL) for the application—without considering whether or not NoSQL was really needed for this type of application.

The truth is, he wanted to use NoSQL because learning new technology is his passion. That’s all well and good (it’s his nature), but in an ideal world, every technology decision should be based on the needs of the business.

That begs the question of what data science can and should be used for. Let’s look at some detailed business driven use cases.

Equity Trading. High-frequency equity trading is a great example of a use for data science. The majority of equity trading today uses algorithms that, increasingly, take into account signals from social media networks and news websites to improve buy and sell decisions.

Understanding customer behavior patterns. Data science can expand traditional data sets with social media data, browser logs, text analytics, and sensor data to produce a more complete picture of customers. Telecom companies can better predict customer churn, retailers can predict which products will sell quickly, and insurance companies can understand how well their customers drive. Even political campaigns are being optimized through big data analytics.

Optimizing retail business processes. Supply chain and delivery routes can be optimized through data science. Retailers can optimize stock purchases based on predictions generated from social media data, web search trends, and weather forecasts. Similarly, geographic positioning and radio frequency identification sensors can be used to track goods or delivery vehicles, and routes can be improved through the integration of real-time traffic information.

Advancing life sciences. Computing power lets us decode entire DNA strings in minutes. In the future, it will help us better predict disease patterns and develop cures. Imagine what would happen if the data from every smart watch and wearable device could be gathered and analyzed—clinical trials of the future would not be limited to small sample sizes. Data science is already being used to record and analyze the heart rhythms and breathing patterns of babies in neonatal ICUs, enabling algorithms that can predict infections 24 hours before physical symptoms appear.

Automation of machines and devices. Data science helps machines and devices become smarter and more autonomous. For example, big data is behind the software used to operate Google’s self-driving car. Similar big data tools can be used to optimize energy grids using data from smart meters. We can even use big data tools to optimize the performance of computers and data warehouses.

Helping security and law agencies. Data science is being applied to improving security and enabling law enforcement. Everyone has heard of the NSA’s use of big data to foil terrorist plots; these techniques are also used to detect and prevent cyber-attacks. Local police units can use data science techniques/tools to catch criminals and even predict criminal activity in much the same way as credit card companies use them to detect fraudulent transactions.

Creating smart cities. Data science is at the heart of the technology being used to create smart cities—for example, letting municipalities optimize traffic flow based on real-time traffic information in conjunction with social media and weather data.

From these examples, it’s possible to imagine a world in which connections between people and systems are much more fluid (smart cities merge into smart homes to form the basis for a more connected society). Indeed, that future is emerging now as the Internet of Things, which is the subject of my upcoming post.

Post Date: 11.12.2015

Prakash Mishra

About the author

Prakash Mishra leads NTT Data’s Data Architecture and Management Practice. A solutions-driven, results-oriented, self-motivated leader, Prakash has a proven record of extensive data architecture leadership in a complex environment. Prakash has been involved in developing and leading the implementation of traditional and innovative big data strategies and solutions, data modernization and master data management solutions for small to large organizations. Prakash is a master in building and motivating high-performance teams, cultivating a positive work environment and promoting a spirit of teamwork and idea-sharing to maximize individual contributions. Prakash holds a master’s degree in computer science , with two decades of experience specialized in enterprise data architecture and management.